The streaming capacity of sparsely-connected P2P systems with distributed control

Peer-to-Peer (P2P) streaming technologies can take advantage of the upload capacity of clients, and hence can scale to large content distribution networks with lower cost. A fundamental question for P2P streaming systems is the maximum streaming rate that all users can sustain. Prior works have studied the optimal streaming rate for a complete network, where every peer is assumed to communicate with all other peers. This is however an impractical assumption in real systems. In this paper, we are interested in the achievable streaming rate when each peer can only connect to a small number of neighbors. We show that even with a random peer selection algorithm and uniform rate allocation, as long as each peer maintains Ω(logN) downstream neighbors, where N is the total number of peers in the system, the system can asymptotically achieve a streaming rate that is close to the optimal streaming rate of a complete network.We then extend our analysis to multi-channel P2P networks, and we study the scenario where "helpers" from channels with excessive upload capacity can help peers in channels with insufficient upload capacity. We show that by letting each peer select Ω(logN) neighbors randomly from either the peers in the same channel or from the helpers, we can achieve a close-to-optimal streaming capacity region. Simulation results are provided to verify our analysis. © 2011 IEEE.published_or_final_versionThe IEEE INFOCOM 2011, Shanghai, China, 10-15 April 2011. In Conference Proceedings, 2011, p. 1449-145

Capacity of P2P on-demand streaming with simple, robust and decentralized control

The performance of large-scaled peer-to-peer (P2P) video-on-demand (VoD) streaming systems can be very challenging to analyze. In practical P2P VoD systems, each peer only interacts with a small number of other peers/neighbors. Further, its upload capacity may vary randomly, and both its downloading position and content availability change dynamically. In this paper, we rigorously study the achievable streaming capacity of large-scale P2P VoD systems with sparse connectivity among peers, and investigate simple and decentralized P2P control strategies that can provably achieve close-to-optimal streaming capacity. We first focus on a single streaming channel. We show that a close-to-optimal streaming rate can be asymptotically achieved for all peers with high probability as the number of peers N increases, by assigning each peer a random set of Θ(log N) neighbors and using a uniform rate-allocation algorithm. Further, the tracker does not need to obtain detailed knowledge of which chunks each peer caches, and hence incurs low overhead. We then study multiple streaming channels where peers watching one channel may help in another channel with insufficient upload bandwidth. We propose a simple random cache-placement strategy, and show that a close-to-optimal streaming capacity region for all channels can be attained with high probability, again with only Θ(logN) per-peer neighbors. These results provide important insights into the dynamics of large-scale P2P VoD systems, which will be useful for guiding the design of improved P2P control protocols. © 2013 IEEE.published_or_final_versio

On dynamic server provisioning in multichannel P2P live streaming

To guarantee the streaming quality in live peer-to-peer (P2P) streaming channels, it is preferable to provision adequate levels of upload capacities at dedicated streaming servers, compensating for peer instability and time-varying peer upload bandwidth availability. Most commercial P2P streaming systems have resorted to the practice of overprovisioning a fixed amount of upload capacity on streaming servers. In this paper, we have performed a detailed analysis on 10 months of run-time traces from UUSee, a commercial P2P streaming system, and observed that available server capacities are not able to keep up with the increasing demand by hundreds of channels. We propose a novel online server capacity provisioning algorithm that proactively adjusts server capacities available to each of the concurrent channels, such that the supply of server bandwidth in each channel dynamically adapts to the forecasted demand, taking into account the number of peers, the streaming quality, and the channel priority. The algorithm is able to learn over time, has full Internet service provider (ISP) awareness to maximally constrain P2P traffic within ISP boundaries, and can provide differentiated streaming qualities to different channels by manipulating their priorities. To evaluate its effectiveness, our experiments are based on an implementation of the algorithm, which replays real-world traces. © 2011 IEEE.published_or_final_versio

Towards Cost-effective On-demand continuous Media Service: A Peer-to- Peer Approach

To overcome the limited bandwidth of streaming servers, Content Distribution Networks (CDNs) are deployed on the edge of the Internet. A large number of such servers have to be installed to make the whole system scalable, making CDN a very expensive way to distribute media. The primary concern of this research is to find an inexpensive way to alleviate the traffic load for media streaming on the servers in a continuous media service infrastructure. Our approach to solve the above problem is motivated by the emerging concept of peer-to-peer computing. Specifically, we let clients that obtained a media object act as streaming servers for following requests to that media object. Unlike the server/client scheme, peers are heterogeneous in the storage capacity and out-bound bandwidth they can contribute. Secondly, peers are heterogeneous in the duration of their commitment to the community. In our research, we identified the following problems in the context of peer-to-peer streaming: 1) How does the come-and-go behaviors of peers affect the system performance? 2). How do we manage the limited resources contributed by each peer? 3). The design of a peer-to-peer streaming protocol that handles peer failure. Solutions to these problems are described and analyzed. 1

On the Limit of Fountain MDC Codes for Video Peer-To-Peer Networks

Video streaming for heterogeneous types of devices, where nodes have different devices characteristics in terms of computational capacity and display, is usually handled by encoding the video with different qualities. This is not well suited for Peer-To-Peer (P2P) systems, as a single peer group can only share content of the same quality, thus limiting the peer group size and efficiency. To address this problem, several existing works propose the use of Multiple Descriptions Coding (MDC). The concept of this type of video codec is to split a video in a number of descriptions which can be used on their own, or aggregated to improve the global quality of the video. Unfortunately existing MDC codes are not flexible, as the video is split in a defined number of descriptions. In this paper, we focus on the practical feasibility of using a Fountain MDC code with properties similar to existing Fountain erasure codes, including the ability to create any number of descriptions when needed (on the fly). We perform simulations using selected pictures to assess the feasibility of using these codes, knowing that they should improve the availability of the video pieces in a P2P system and hence the video streaming quality. We observe that, although this idea seems promising, the evaluated benefits, demonstrated by the PSNR values, are limited when used in a real P2P video streaming system

A delay-based aggregate rate control for P2P streaming systems

In this paper we consider mesh based P2P streaming systems focusing on the problem of regulating peer transmission rate to match the system demand while not overloading each peer upload link capacity. We propose Hose Rate Control (HRC), a novel scheme to control the speed at which peers offer chunks to other peers, ultimately controlling peer uplink capacity utilization. This is of critical importance for heterogeneous scenarios like the one faced in the Internet, where peer upload capacity is unknown and varies widely. HRC nicely adapts to the actual peer available upload bandwidth and system demand, so that Quality of Experience is greatly enhanced. To support our claims we present both simulations and actual experiments involving more than 1000 peers to assess performance in real scenarios. Results show that HRC consistently outperforms the Quality of Experience achieved by non-adaptive scheme

On Resource Aware Algorithms in Epidemic Live Streaming

Epidemic-style diffusion schemes have been previously proposed for achieving peer-to-peer live streaming. Their performance trade-offs have been deeply analyzed for homogeneous systems, where all peers have the same upload capacity. However, epidemic schemes designed for heterogeneous systems have not been completely understood yet. In this report we focus on the peer selection process and propose a generic model that encompasses a large class of algorithms. The process is modeled as a combination of two functions, an aware one and an agnostic one. By means of simulations, we analyze the awareness-agnostism trade-offs on the peer selection process and the impact of the source distribution policy in non-homogeneous networks. We highlight that the early diffusion of a given chunk is crucial for its overall diffusion performance, and a fairness trade-off arises between the performance of heterogeneous peers, as a function of the level of awareness